Setup

knitr::opts_chunk$set(echo = TRUE)

library(tidyverse)
library(R.utils)
library(wbCorr)
library(readxl)
library(kableExtra)
library(brms)
library(bayesplot)

top_directory <- file.path(
  'C:', 'Users', 'kueng', 'OneDrive - Universität Zürich UZH',
  '04 Papers', '02 T&T Control', 'Analysis', 'ACTIVITY', 'BRMS'
)

working_directory <- file.path(top_directory, 'SensitivityCovariates')
setwd(working_directory)

functions_directory <- file.path('C:', 'Users', 'kueng', 
                                 'OneDrive - Universität Zürich UZH', 
                                 'RFunctions')

source(file.path(functions_directory, 'ReportModels.R'))
source(file.path(functions_directory, 'PrettyTables.R'))
source(file.path(functions_directory, 'ReportMeasures.R'))
source(file.path(top_directory, 'Functions', 'PrepareData.R'))
system("shutdown /a")
## [1] 1116
# Set options for analysis
use_mi = FALSE
shutdown = FALSE
report_ordinal = FALSE

options(
  dplyr.print_max = 100, 
  brms.backend = 'cmdstan',
  brms.file_refit = ifelse(use_mi, 'never', 'on_change'),
  error = function() beepr::beep(sound = 5)
)
df <- openxlsx::read.xlsx(file.path(working_directory, 'long.xlsx'))
df_original <- df

df_double <- prepare_data(df, use_mi = use_mi)[[1]]

Constructing scales Re-coding pusing reshaping data (4field) centering data within and between

Modelling

# For indistinguishable Dyads
model_rows_fixed <- c(
    'Intercept', 
    # '-- WITHIN PERSON MAIN EFFECTS --', 
    'persuasion_self_cw', 
    'persuasion_partner_cw', 
    'pressure_self_cw', 
    'pressure_partner_cw', 
    'pushing_self_cw', 
    'pushing_partner_cw', 
    'day', 
    'weartime_self_cw',
    
    'support_self_cw',
    'support_partner_cw',
    'isWeekendWeekend',
    'got_JITAI_selfJITAIreceived',
    'skilled_supportDaysafterIntervention',
    
    
    # '-- BETWEEN PERSON MAIN EFFECTS',
    'persuasion_self_cb',
    'persuasion_partner_cb',
    'pressure_self_cb',
    'pressure_partner_cb',
    'pushing_self_cb',
    'pushing_partner_cb',
    'weartime_self_cb',
    'studyGroupFirst3weeksinterventions',
    'studyGrouplast3weeksinterventions'
  )


model_rows_fixed_ordinal <- c(
  model_rows_fixed[1],
  'Intercept[1]',
  'Intercept[2]',
  'Intercept[3]',
  'Intercept[4]',
  'Intercept[5]',
  model_rows_fixed[2:length(model_rows_fixed)]
)

model_rows_random <- c(
  # '--------------',
  # '-- RANDOM EFFECTS --',
  'sd(Intercept)', 
  'sd(persuasion_self_cw)',
  'sd(persuasion_partner_cw)',
  'sd(pressure_self_cw)',
  'sd(pressure_partner_cw)',
  'sd(pushing_self_cw)',
  'sd(pushing_partner_cw)',
  # '-- CORRELATION STRUCTURE -- ', 
  'ar[1]', 
  'nu',
  'shape',
  'sderr',
  'sigma'
)

model_rows_random_ordinal <- c(model_rows_random,'disc')
# For indistinguishable Dyads
model_rownames_fixed <- c(
    'Intercept', 
    # '-- WITHIN PERSON MAIN EFFECTS --', 
    'Daily perceived persuasion target -> target', 
    'Daily perceived persuasion target -> agent', 
    'Daily perceived pressure target -> target', 
    'Daily perceived pressure target -> agent', 
    'Daily perceived pushing target -> target', 
    'Daily perceived pushing target -> agent', 
    'Day', 
    'Daily weartime',
    
    'Daily perceived support target -> target',
    'Daily perceived support target -> agent',
    'Is a weekend',
    'JITAI received',
    'Days post skilled support intervention',
    
    # '-- BETWEEN PERSON MAIN EFFECTS',
    'Mean perceived persuasion target -> target',
    'Mean Perceived persuasion target -> agent',
    'Mean Perceived pressure target -> target',
    'Mean Perceived pressure target -> agent',
    'Mean Perceived pushing target -> target',
    'Mean Perceived pushing target -> agent',
    'Mean weartime',
    
    'Difference study group 2',
    'Difference study group 3'
  )


model_rownames_fixed_ordinal <- c(
  model_rownames_fixed[1],
  'Intercept[1]',
  'Intercept[2]',
  'Intercept[3]',
  'Intercept[4]',
  'Intercept[5]',
  model_rownames_fixed[2:length(model_rownames_fixed)]
)

model_rownames_random <- c(
  # '--------------',
  # '-- RANDOM EFFECTS --',
  'sd(Intercept)', 
  'sd(Daily perceived persuasion target -> target)', 
  'sd(Daily perceived persuasion target -> agent)', 
  'sd(Daily perceived pressure target -> target)', 
  'sd(Daily perceived pressure target -> agent)', 
  'sd(Daily perceived pushing target -> target)', 
  'sd(Daily perceived pushing target -> agent)', 
  # '-- CORRELATION STRUCTURE -- ', 
  'ar[1]', 
  'nu',
  'shape',
  'sderr',
  'sigma'
)

model_rownames_random_ordinal <- c(model_rownames_random,'disc')
rows_to_pack <- list(
  "Within-Person Effects" = c(2,14),
  "Between-Person Effects" = c(15,23),
  "Random Effects" = c(24, 30), 
  "Additional Parameters" = c(31,35)
  )

rows_to_pack_ordinal <- list(
  "Intercepts" = c(1,6),
  "Within-Person Effects" = c(2+5,14+5),
  "Between-Person Effects" = c(15+5,23+5),
  "Random Effects" = c(24+5, 30+5), 
  "Additional Parameters" = c(31+5,35+6)
  )

Subjective MVPA

range(df_double$pa_sub, na.rm = T) 
## [1]   0 720
hist(df_double$pa_sub, breaks = 100)

Modelling using the gaussian family fails. Due to the many zeros, transformations won’t help estimating the models. We employ the negative binomial family.

formula <- bf(
  pa_sub ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    #barriers_self_cw +
    
    support_self_cw + support_partner_cw + 
    #comf_self_cw + reas_self_cw + 
    isWeekend + 
    got_JITAI_self + skilled_support +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    
    studyGroup +
    
    day + 
    
    # Random effects
    (persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | coupleID),

  autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)




prior1 <- c(
  brms::set_prior("normal(0, 2.5)", class = "b"),
  brms::set_prior("normal(0, 50)", class = "Intercept", lb = 0),
  
  brms::set_prior("normal(0, 10)", class = "sd", group = "coupleID", lb = 0),
  
  brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1),
  brms::set_prior("cauchy(0, 20)", class = "shape"), 
  brms::set_prior("cauchy(0, 10)", class='sderr')
)

#df_minimal <- df_double[, c("userID", all.vars(as.formula(formula)))]

pa_sub <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = brms::negbinomial(),
  #control = list(adapt_delta = 0.99),
  iter = 5000,
  warmup = 2000,
  chains = 4,
  cores = 4,
  seed = 7777,
  file = file.path(working_directory, "models_cache", "pa_sub")
)
pp_check(pa_sub, type='hist')
## Using 10 posterior draws for ppc type 'hist' by default.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

pp_check(pa_sub)
## Using 10 posterior draws for ppc type 'dens_overlay' by default.

loo(pa_sub)
## 
## Computed from 12000 by 3732 log-likelihood matrix.
## 
##          Estimate    SE
## elpd_loo -12014.0 177.5
## p_loo        35.8   3.6
## looic     24027.9 355.0
## ------
## MCSE of elpd_loo is NA.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.3, 1.8]).
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. ESS
## (-Inf, 0.7]   (good)     3729  99.9%   707     
##    (0.7, 1]   (bad)         3   0.1%   <NA>    
##    (1, Inf)   (very bad)    0   0.0%   <NA>    
## See help('pareto-k-diagnostic') for details.
plot(pa_sub, ask = FALSE)

summarize_brms(
  pa_sub, 
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = T) %>%
  print_df(rows_to_pack = rows_to_pack)
IRR l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 26.72* 17.32 41.49 1.001 2961.37 5150.66
Within-Person Effects
Daily perceived persuasion target -> target 1.05 0.94 1.18 1.000 10158.84 8909.68
Daily perceived persuasion target -> agent 1.04 0.93 1.17 1.000 10462.74 8729.24
Daily perceived pressure target -> target 0.94 0.71 1.28 1.000 8512.47 7411.87
Daily perceived pressure target -> agent 1.21 0.90 1.67 1.001 10442.05 7943.66
Daily perceived pushing target -> target 1.08 0.89 1.34 1.000 7154.35 7887.45
Daily perceived pushing target -> agent 1.06 0.90 1.27 1.000 10919.29 8856.10
Day 0.96 0.62 1.46 1.000 7618.10 7886.93
Daily weartime NA NA NA NA NA NA
Daily perceived support target -> target 1.25* 1.17 1.35 1.000 12188.25 8990.54
Daily perceived support target -> agent 1.21* 1.11 1.31 1.000 11562.51 8885.56
Is a weekend 1.22* 1.01 1.50 1.000 12722.15 8968.39
JITAI received 0.75* 0.58 0.97 1.001 12386.45 9447.34
Days post skilled support intervention 0.86 0.62 1.19 1.000 7682.55 8781.26
Between-Person Effects
Mean perceived persuasion target -> target 1.65 0.83 3.26 1.000 2587.15 5108.08
Mean Perceived persuasion target -> agent 1.17 0.60 2.32 1.001 2668.07 5088.51
Mean Perceived pressure target -> target 0.54 0.24 1.21 1.001 4074.67 6709.82
Mean Perceived pressure target -> agent 0.42* 0.19 0.95 1.000 3767.82 5661.90
Mean Perceived pushing target -> target 1.53 0.55 4.34 1.001 3525.25 5461.59
Mean Perceived pushing target -> agent 2.03 0.73 5.72 1.001 3660.47 5917.92
Mean weartime NA NA NA NA NA NA
Difference study group 2 0.80 0.46 1.37 1.001 2650.64 4306.40
Difference study group 3 1.12 0.64 1.94 1.002 2693.03 5043.26
Random Effects
sd(Intercept) 0.64 0.47 0.87 1.00 3450.52 6368.11
sd(Daily perceived persuasion target -> target) 0.07 0.00 0.21 1.00 4444.04 4171.61
sd(Daily perceived persuasion target -> agent) 0.07 0.00 0.18 1.00 5615.70 5271.33
sd(Daily perceived pressure target -> target) 0.17 0.01 0.53 1.00 6058.90 4675.72
sd(Daily perceived pressure target -> agent) 0.18 0.01 0.51 1.00 6012.32 4727.56
sd(Daily perceived pushing target -> target) 0.26 0.01 0.56 1.00 2781.12 2723.71
sd(Daily perceived pushing target -> agent) 0.11 0.00 0.30 1.00 5418.09 4813.41
Additional Parameters
ar[1] 0.03 -0.94 0.94 1.00 9013.24 6700.96
nu NA NA NA NA NA NA
shape 0.14 0.13 0.15 1.00 14505.42 9416.17
sderr 0.05 0.00 0.14 1.00 5181.12 4714.47
sigma NA NA NA NA NA NA

Device Based MVPA

range(df_double$pa_obj, na.rm = T) 
## [1]   5.75 971.25
hist(df_double$pa_obj, breaks = 50)

df_double$pa_obj_log <- log(df_double$pa_obj)

hist(df_double$pa_obj_log, breaks = 50)

We tried negative binomial here as well for consistency, but the model did not converge. Poisson also did not work. As we have no zeros in this distribution, we log transform.

formula <- bf(
  pa_obj_log ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    #barriers_self_cw +
    
    support_self_cw + support_partner_cw + 
    #comf_self_cw + reas_self_cw + 
    isWeekend + 
    got_JITAI_self + skilled_support +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    
    studyGroup +
    
    day + weartime_self_cw + weartime_self_cb +
    
    # Random effects
    (persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | coupleID),

  autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)



prior1 <- c(
  brms::set_prior("normal(0, 2.5)", class = "b"),
  brms::set_prior("normal(0, 50)", class = "Intercept", lb = 0),
  
  brms::set_prior("normal(0, 10)", class = "sd", group = "coupleID", lb = 0),
  
  brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1),
  brms::set_prior("cauchy(0, 10)", class = "sigma", lb = 0)
)


#df_minimal <- df_double[, c("userID", all.vars(as.formula(formula)))]

pa_obj_log <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = gaussian(),
  #control = list(adapt_delta = 0.99),
  iter = 5000,
  warmup = 2000,
  chains = 4,
  cores = 4,
  seed = 7777,
  file = file.path(working_directory, "models_cache", "pa_obj_log")
)
# plotting with the first imputed dataset. 
pp_check(pa_obj_log, type='hist')
## Using 10 posterior draws for ppc type 'hist' by default.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

pp_check(pa_obj_log)
## Using 10 posterior draws for ppc type 'dens_overlay' by default.

loo(pa_obj_log)
## 
## Computed from 12000 by 3333 log-likelihood matrix.
## 
##          Estimate    SE
## elpd_loo  -2794.2  57.2
## p_loo        95.1   4.7
## looic      5588.4 114.4
## ------
## MCSE of elpd_loo is 0.1.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.3, 1.5]).
## 
## All Pareto k estimates are good (k < 0.7).
## See help('pareto-k-diagnostic') for details.
plot(pa_obj_log, ask = FALSE)

summarize_brms(
  pa_obj_log, 
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = T) %>%
  print_df(rows_to_pack = rows_to_pack)
exp(Est.) l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 121.23* 100.82 145.60 1.000 4060.10 6293.50
Within-Person Effects
Daily perceived persuasion target -> target 1.01 0.98 1.04 1.000 11202.97 9404.56
Daily perceived persuasion target -> agent 1.00 0.97 1.03 1.001 13782.61 9627.01
Daily perceived pressure target -> target 0.95 0.89 1.01 1.000 14202.51 9489.78
Daily perceived pressure target -> agent 0.98 0.92 1.04 1.000 16982.92 8501.21
Daily perceived pushing target -> target 1.02 0.97 1.06 1.001 11888.59 8676.31
Daily perceived pushing target -> agent 1.02 0.98 1.06 1.000 16756.29 9284.65
Day 0.95 0.84 1.07 1.000 16821.49 8768.23
Daily weartime 1.00* 1.00 1.00 1.000 12148.79 8015.43
Daily perceived support target -> target 1.04* 1.02 1.05 1.001 19687.23 8578.81
Daily perceived support target -> agent 1.02* 1.00 1.04 1.000 20313.58 9388.57
Is a weekend 1.05* 1.01 1.10 1.001 19953.90 9028.63
JITAI received 0.93* 0.88 0.98 1.000 19481.13 9256.96
Days post skilled support intervention 1.03 0.94 1.12 1.000 15550.23 9456.64
Between-Person Effects
Mean perceived persuasion target -> target 1.07 0.80 1.46 1.001 3696.54 5491.87
Mean Perceived persuasion target -> agent 0.94 0.69 1.30 1.001 3670.41 5871.89
Mean Perceived pressure target -> target 1.02 0.73 1.42 1.001 5261.88 8442.33
Mean Perceived pressure target -> agent 0.98 0.72 1.34 1.002 4769.62 6557.83
Mean Perceived pushing target -> target 1.02 0.66 1.57 1.000 5582.80 7367.89
Mean Perceived pushing target -> agent 1.33 0.85 2.05 1.001 5447.49 7501.21
Mean weartime 1.00 1.00 1.00 1.000 15026.58 11061.83
Difference study group 2 0.89 0.69 1.15 1.000 3789.44 5681.45
Difference study group 3 0.97 0.75 1.26 1.000 3566.36 6129.10
Random Effects
sd(Intercept) 0.31 0.23 0.40 1.00 3752.77 6223.68
sd(Daily perceived persuasion target -> target) 0.05 0.02 0.08 1.00 6222.14 7438.24
sd(Daily perceived persuasion target -> agent) 0.05 0.02 0.08 1.00 5379.76 4726.82
sd(Daily perceived pressure target -> target) 0.05 0.00 0.14 1.00 5794.29 6753.92
sd(Daily perceived pressure target -> agent) 0.03 0.00 0.10 1.00 7900.31 6853.72
sd(Daily perceived pushing target -> target) 0.06 0.00 0.14 1.00 3065.03 6160.59
sd(Daily perceived pushing target -> agent) 0.03 0.00 0.08 1.00 6210.15 6587.85
Additional Parameters
ar[1] 0.30 0.26 0.33 1.00 19387.01 8198.04
nu NA NA NA NA NA NA
shape NA NA NA NA NA NA
sderr NA NA NA NA NA NA
sigma 0.55 0.54 0.57 1.00 17059.91 9199.99 
#summary(pa_obj_log)

Affect

range(df_double$aff, na.rm = T) 
## [1] 1 6
hist(df_double$aff, breaks = 15)

formula <- bf(
  aff ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    #barriers_self_cw +
    
    support_self_cw + support_partner_cw + 
    #comf_self_cw + reas_self_cw + 
    isWeekend + 
    got_JITAI_self + skilled_support +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    
    studyGroup +
    
    day +
    
    # Random effects
    (persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | coupleID),

  autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)



prior1 <- c(
  brms::set_prior("normal(0, 5)", class = "b"),
  brms::set_prior("normal(0, 20)", class = "Intercept", lb=0, ub=6), # range of the outcome scale
  
  brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0),

  brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1),
  brms::set_prior("cauchy(0, 10)", class = "sigma", lb = 0)
  
)


df_minimal <- df_double[, c("userID", all.vars(as.formula(formula)))]

mood <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = gaussian(),
  #control = list(adapt_delta = 0.95, max_treedepth = 15),
  iter = 5000,
  warmup = 2000,
  chains = 4,
  cores = 4,
  seed = 7777,
  file = file.path(working_directory, "models_cache", "mood")
)
pp_check(mood, type='hist')
## Using 10 posterior draws for ppc type 'hist' by default.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

pp_check(mood)
## Using 10 posterior draws for ppc type 'dens_overlay' by default.

loo(mood)
## 
## Computed from 12000 by 3732 log-likelihood matrix.
## 
##          Estimate    SE
## elpd_loo  -4788.7  63.9
## p_loo        94.2   4.8
## looic      9577.4 127.8
## ------
## MCSE of elpd_loo is 0.1.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.5, 1.5]).
## 
## All Pareto k estimates are good (k < 0.7).
## See help('pareto-k-diagnostic') for details.
plot(mood, ask = FALSE)

summarize_brms(
  mood, 
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = F) %>%
  print_df(rows_to_pack = rows_to_pack)
b l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 4.66* 4.33 4.98 1.001 5333.50 7555.67
Within-Person Effects
Daily perceived persuasion target -> target -0.04* -0.07 0.00 1.000 17851.79 9377.35
Daily perceived persuasion target -> agent -0.01 -0.05 0.03 1.000 15771.31 9712.42
Daily perceived pressure target -> target -0.06 -0.18 0.05 1.000 13301.84 8210.54
Daily perceived pressure target -> agent -0.03 -0.17 0.09 1.001 12005.89 8219.22
Daily perceived pushing target -> target 0.00 -0.06 0.06 1.000 16381.60 9400.70
Daily perceived pushing target -> agent 0.05 -0.01 0.10 1.001 17264.82 10239.34
Day 0.24* 0.03 0.46 1.001 19351.37 9299.19
Daily weartime NA NA NA NA NA NA
Daily perceived support target -> target 0.08* 0.05 0.10 1.000 23913.91 9489.23
Daily perceived support target -> agent 0.03* 0.00 0.05 1.001 24434.47 9707.31
Is a weekend 0.10* 0.04 0.17 1.001 25691.27 8223.61
JITAI received -0.08* -0.16 -0.01 1.001 23978.67 8316.33
Days post skilled support intervention 0.01 -0.14 0.17 1.001 18552.03 9823.06
Between-Person Effects
Mean perceived persuasion target -> target 0.38 -0.16 0.94 1.001 4243.21 6996.33
Mean Perceived persuasion target -> agent 0.30 -0.25 0.85 1.001 4329.37 6922.11
Mean Perceived pressure target -> target -0.32 -0.89 0.24 1.001 5643.10 8053.99
Mean Perceived pressure target -> agent -0.28 -0.85 0.29 1.001 5831.13 8614.96
Mean Perceived pushing target -> target 0.17 -0.60 0.94 1.000 6173.45 7735.82
Mean Perceived pushing target -> agent 0.28 -0.50 1.05 1.001 5957.39 7327.67
Mean weartime NA NA NA NA NA NA
Difference study group 2 -0.23 -0.67 0.22 1.000 5150.59 7479.79
Difference study group 3 0.36 -0.11 0.81 1.001 5116.81 6938.44
Random Effects
sd(Intercept) 0.55 0.42 0.72 1.00 4671.17 6976.73
sd(Daily perceived persuasion target -> target) 0.03 0.00 0.07 1.00 5883.93 6260.34
sd(Daily perceived persuasion target -> agent) 0.06 0.01 0.11 1.00 3841.53 4179.94
sd(Daily perceived pressure target -> target) 0.12 0.01 0.30 1.00 4294.86 6686.57
sd(Daily perceived pressure target -> agent) 0.17 0.02 0.36 1.00 3932.17 4325.13
sd(Daily perceived pushing target -> target) 0.08 0.01 0.17 1.00 4416.36 3438.63
sd(Daily perceived pushing target -> agent) 0.04 0.00 0.12 1.00 6352.21 7697.95
Additional Parameters
ar[1] 0.45 0.42 0.48 1.00 20935.05 9314.02
nu NA NA NA NA NA NA
shape NA NA NA NA NA NA
sderr NA NA NA NA NA NA
sigma 0.86 0.84 0.88 1.00 20987.51 7558.74

reactance

range(df_double$reactance, na.rm = T) 
## [1] 0 5
hist(df_double$reactance, breaks = 6)

formula <- bf(
  reactance ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    #barriers_self_cw +
    
    support_self_cw + support_partner_cw + 
    #comf_self_cw + reas_self_cw + 
    isWeekend + 
    got_JITAI_self + skilled_support +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    
    studyGroup +
    
    day +
    
    # Random effects
    (persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | coupleID),

  autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)



prior1 <- c(
  brms::set_prior("normal(0, 5)", class = "b"),
  brms::set_prior("normal(0, 20)", class = "Intercept", lb=0, ub=5), # range of the outcome scale
  
  brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0),

  brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1),
  brms::set_prior("cauchy(0, 10)", class = "sigma", lb = 0)
)


df_minimal <- df_double[, c("userID", all.vars(as.formula(formula)))]

reactance <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = gaussian(),
  #control = list(adapt_delta = 0.95, max_treedepth = 15),
  iter = 5000,
  warmup = 2000,
  chains = 4,
  cores = 4,
  seed = 7777,
  file = file.path(working_directory, "models_cache", "reactance")

)
pp_check(reactance, type='hist')
## Using 10 posterior draws for ppc type 'hist' by default.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

pp_check(reactance)
## Using 10 posterior draws for ppc type 'dens_overlay' by default.

loo(reactance)
## 
## Computed from 12000 by 755 log-likelihood matrix.
## 
##          Estimate   SE
## elpd_loo  -1057.4 35.1
## p_loo        75.9  7.3
## looic      2114.7 70.3
## ------
## MCSE of elpd_loo is NA.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.4, 1.4]).
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. ESS
## (-Inf, 0.7]   (good)     747   98.9%   202     
##    (0.7, 1]   (bad)        8    1.1%   <NA>    
##    (1, Inf)   (very bad)   0    0.0%   <NA>    
## See help('pareto-k-diagnostic') for details.
plot(reactance, ask = FALSE)

summary(reactance)
##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: reactance ~ persuasion_self_cw + persuasion_partner_cw + pressure_self_cw + pressure_partner_cw + pushing_self_cw + pushing_partner_cw + support_self_cw + support_partner_cw + isWeekend + got_JITAI_self + skilled_support + persuasion_self_cb + persuasion_partner_cb + pressure_self_cb + pressure_partner_cb + pushing_self_cb + pushing_partner_cb + studyGroup + day + (persuasion_self_cw + persuasion_partner_cw + pressure_self_cw + pressure_partner_cw + pushing_self_cw + pushing_partner_cw | coupleID) 
##          autocor ~ ar(time = day, gr = coupleID:userID, p = 1)
##    Data: data (Number of observations: 755) 
##   Draws: 4 chains, each with iter = 5000; warmup = 2000; thin = 1;
##          total post-warmup draws = 12000
## 
## Correlation Structures:
##       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## ar[1]     0.02      0.04    -0.06     0.11 1.00     9970     8716
## 
## Multilevel Hyperparameters:
## ~coupleID (Number of levels: 38) 
##                                                Estimate Est.Error l-95% CI u-95% CI Rhat
## sd(Intercept)                                      0.19      0.08     0.03     0.35 1.00
## sd(persuasion_self_cw)                             0.04      0.03     0.00     0.11 1.00
## sd(persuasion_partner_cw)                          0.04      0.03     0.00     0.13 1.00
## sd(pressure_self_cw)                               0.39      0.10     0.22     0.61 1.00
## sd(pressure_partner_cw)                            0.21      0.15     0.01     0.57 1.00
## sd(pushing_self_cw)                                0.12      0.06     0.01     0.23 1.00
## sd(pushing_partner_cw)                             0.05      0.04     0.00     0.15 1.00
## cor(Intercept,persuasion_self_cw)                 -0.22      0.36    -0.81     0.53 1.00
## cor(Intercept,persuasion_partner_cw)               0.06      0.34    -0.61     0.70 1.00
## cor(persuasion_self_cw,persuasion_partner_cw)     -0.03      0.35    -0.69     0.64 1.00
## cor(Intercept,pressure_self_cw)                    0.03      0.31    -0.57     0.61 1.00
## cor(persuasion_self_cw,pressure_self_cw)          -0.00      0.34    -0.64     0.64 1.00
## cor(persuasion_partner_cw,pressure_self_cw)        0.00      0.35    -0.66     0.65 1.00
## cor(Intercept,pressure_partner_cw)                 0.04      0.33    -0.59     0.66 1.00
## cor(persuasion_self_cw,pressure_partner_cw)        0.05      0.36    -0.64     0.70 1.00
## cor(persuasion_partner_cw,pressure_partner_cw)    -0.01      0.35    -0.67     0.66 1.00
## cor(pressure_self_cw,pressure_partner_cw)         -0.16      0.32    -0.71     0.51 1.00
## cor(Intercept,pushing_self_cw)                     0.15      0.32    -0.49     0.73 1.00
## cor(persuasion_self_cw,pushing_self_cw)           -0.04      0.35    -0.68     0.63 1.00
## cor(persuasion_partner_cw,pushing_self_cw)         0.09      0.35    -0.60     0.72 1.00
## cor(pressure_self_cw,pushing_self_cw)             -0.13      0.31    -0.68     0.49 1.00
## cor(pressure_partner_cw,pushing_self_cw)           0.05      0.35    -0.63     0.70 1.00
## cor(Intercept,pushing_partner_cw)                  0.04      0.35    -0.64     0.67 1.00
## cor(persuasion_self_cw,pushing_partner_cw)         0.00      0.35    -0.65     0.66 1.00
## cor(persuasion_partner_cw,pushing_partner_cw)     -0.03      0.36    -0.70     0.65 1.00
## cor(pressure_self_cw,pushing_partner_cw)          -0.06      0.35    -0.70     0.61 1.00
## cor(pressure_partner_cw,pushing_partner_cw)        0.02      0.36    -0.66     0.69 1.00
## cor(pushing_self_cw,pushing_partner_cw)            0.05      0.35    -0.64     0.70 1.00
##                                                Bulk_ESS Tail_ESS
## sd(Intercept)                                      2898     2874
## sd(persuasion_self_cw)                             3930     5676
## sd(persuasion_partner_cw)                          4487     4544
## sd(pressure_self_cw)                               5865     7215
## sd(pressure_partner_cw)                            3081     5104
## sd(pushing_self_cw)                                2958     3046
## sd(pushing_partner_cw)                             7151     5983
## cor(Intercept,persuasion_self_cw)                  8156     8739
## cor(Intercept,persuasion_partner_cw)              12830     8935
## cor(persuasion_self_cw,persuasion_partner_cw)     11546     9110
## cor(Intercept,pressure_self_cw)                    3298     5245
## cor(persuasion_self_cw,pressure_self_cw)           3200     5698
## cor(persuasion_partner_cw,pressure_self_cw)        2918     4873
## cor(Intercept,pressure_partner_cw)                 8214     8303
## cor(persuasion_self_cw,pressure_partner_cw)        8112     9219
## cor(persuasion_partner_cw,pressure_partner_cw)     8244     9072
## cor(pressure_self_cw,pressure_partner_cw)          9917     8359
## cor(Intercept,pushing_self_cw)                     5937     8092
## cor(persuasion_self_cw,pushing_self_cw)            5419     7694
## cor(persuasion_partner_cw,pushing_self_cw)         5235     7804
## cor(pressure_self_cw,pushing_self_cw)              8008     9156
## cor(pressure_partner_cw,pushing_self_cw)           5960     9732
## cor(Intercept,pushing_partner_cw)                 15007     8839
## cor(persuasion_self_cw,pushing_partner_cw)        13519     9399
## cor(persuasion_partner_cw,pushing_partner_cw)     10267     8818
## cor(pressure_self_cw,pushing_partner_cw)          12715     9426
## cor(pressure_partner_cw,pushing_partner_cw)        9517     8910
## cor(pushing_self_cw,pushing_partner_cw)           10551     9877
## 
## Regression Coefficients:
##                                      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept                                0.69      0.13     0.45     0.94 1.00     9782
## persuasion_self_cw                      -0.02      0.03    -0.08     0.04 1.00    11948
## persuasion_partner_cw                    0.02      0.03    -0.05     0.09 1.00    11719
## pressure_self_cw                         0.25      0.11     0.04     0.46 1.00     6527
## pressure_partner_cw                      0.13      0.11    -0.06     0.36 1.00     6266
## pushing_self_cw                          0.11      0.04     0.02     0.20 1.00     8233
## pushing_partner_cw                      -0.00      0.04    -0.09     0.08 1.00    13127
## support_self_cw                         -0.08      0.03    -0.13    -0.02 1.00    13393
## support_partner_cw                      -0.02      0.03    -0.08     0.03 1.00    14088
## isWeekendWeekend                        -0.12      0.08    -0.28     0.03 1.00    15902
## got_JITAI_selfJITAIreceived             -0.01      0.11    -0.22     0.20 1.00    13319
## skilled_supportDaysafterIntervention    -0.09      0.13    -0.34     0.17 1.00     8060
## persuasion_self_cb                       0.05      0.17    -0.29     0.39 1.00     5949
## persuasion_partner_cb                    0.06      0.19    -0.32     0.44 1.00     6706
## pressure_self_cb                         0.58      0.20     0.20     0.97 1.00     7419
## pressure_partner_cb                      0.24      0.21    -0.18     0.66 1.00     6869
## pushing_self_cb                         -0.19      0.26    -0.69     0.32 1.00     7353
## pushing_partner_cb                      -0.56      0.28    -1.10     0.00 1.00     8683
## studyGroupFirst3weeksinterventions       0.08      0.13    -0.19     0.34 1.00     8011
## studyGrouplast3weeksinterventions       -0.33      0.14    -0.61    -0.05 1.00     6071
## day                                      0.13      0.19    -0.24     0.50 1.00     9201
##                                      Tail_ESS
## Intercept                                9758
## persuasion_self_cw                       9382
## persuasion_partner_cw                    8591
## pressure_self_cw                         7771
## pressure_partner_cw                      5576
## pushing_self_cw                          8965
## pushing_partner_cw                       8637
## support_self_cw                          8760
## support_partner_cw                       9370
## isWeekendWeekend                         9228
## got_JITAI_selfJITAIreceived              9708
## skilled_supportDaysafterIntervention     8869
## persuasion_self_cb                       7203
## persuasion_partner_cb                    7438
## pressure_self_cb                         7886
## pressure_partner_cb                      8105
## pushing_self_cb                          8514
## pushing_partner_cb                       8277
## studyGroupFirst3weeksinterventions       7901
## studyGrouplast3weeksinterventions        7951
## day                                      9223
## 
## Further Distributional Parameters:
##       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma     0.93      0.03     0.88     0.98 1.00    10201     8354
## 
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
summarize_brms(
  reactance, 
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = F) %>%
  print_df(rows_to_pack = rows_to_pack)
b l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 0.69* 0.45 0.94 1.000 9781.55 9757.83
Within-Person Effects
Daily perceived persuasion target -> target -0.02 -0.08 0.04 1.000 11947.63 9382.08
Daily perceived persuasion target -> agent 0.02 -0.05 0.09 1.000 11718.77 8591.41
Daily perceived pressure target -> target 0.25* 0.04 0.46 1.000 6527.47 7771.44
Daily perceived pressure target -> agent 0.13 -0.06 0.36 1.001 6265.78 5575.91
Daily perceived pushing target -> target 0.11* 0.02 0.20 1.000 8233.20 8964.64
Daily perceived pushing target -> agent 0.00 -0.09 0.08 1.000 13126.55 8636.78
Day 0.13 -0.24 0.50 1.000 9200.75 9222.94
Daily weartime NA NA NA NA NA NA
Daily perceived support target -> target -0.08* -0.13 -0.02 1.000 13393.09 8759.59
Daily perceived support target -> agent -0.02 -0.08 0.03 1.000 14087.96 9370.01
Is a weekend -0.12 -0.28 0.03 1.000 15902.22 9228.47
JITAI received -0.01 -0.22 0.20 1.000 13318.51 9708.20
Days post skilled support intervention -0.09 -0.34 0.17 1.000 8060.22 8869.24
Between-Person Effects
Mean perceived persuasion target -> target 0.05 -0.29 0.39 1.000 5948.63 7203.02
Mean Perceived persuasion target -> agent 0.06 -0.32 0.44 1.000 6705.99 7438.37
Mean Perceived pressure target -> target 0.58* 0.20 0.97 1.000 7418.87 7885.74
Mean Perceived pressure target -> agent 0.24 -0.18 0.66 1.001 6869.15 8105.16
Mean Perceived pushing target -> target -0.19 -0.69 0.32 1.000 7352.72 8513.58
Mean Perceived pushing target -> agent -0.56 -1.10 0.00 1.000 8682.65 8277.21
Mean weartime NA NA NA NA NA NA
Difference study group 2 0.08 -0.19 0.34 1.000 8011.43 7900.90
Difference study group 3 -0.33* -0.61 -0.05 1.000 6071.00 7951.32
Random Effects
sd(Intercept) 0.19 0.03 0.35 1.00 2898.25 2873.61
sd(Daily perceived persuasion target -> target) 0.04 0.00 0.11 1.00 3929.52 5675.83
sd(Daily perceived persuasion target -> agent) 0.04 0.00 0.13 1.00 4487.18 4543.64
sd(Daily perceived pressure target -> target) 0.39 0.22 0.61 1.00 5864.69 7215.12
sd(Daily perceived pressure target -> agent) 0.21 0.01 0.57 1.00 3080.53 5104.17
sd(Daily perceived pushing target -> target) 0.12 0.01 0.23 1.00 2957.93 3046.23
sd(Daily perceived pushing target -> agent) 0.05 0.00 0.15 1.00 7151.26 5982.57
Additional Parameters
ar[1] 0.02 -0.06 0.11 1.00 9969.53 8715.68
nu NA NA NA NA NA NA
shape NA NA NA NA NA NA
sderr NA NA NA NA NA NA
sigma 0.93 0.88 0.98 1.00 10200.99 8354.22

Binary Version

introduce_binary_reactance <- function(data) {
  data$is_reactance <- factor(data$reactance > 0, levels = c(FALSE, TRUE), labels = c(0, 1))
  return(data)
}



df_double <- introduce_binary_reactance(df_double)
if (use_mi) {
  for (i in seq_along(implist)) {
    implist[[i]] <- introduce_binary_reactance(implist[[i]])
  }
}


formula <- bf(
  is_reactance ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    #barriers_self_cw +
    
    support_self_cw + support_partner_cw +
    #comf_self_cw + reas_self_cw + 
    isWeekend + 
    got_JITAI_self + skilled_support +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    
    studyGroup +
    
    day +
    
    # Random effects
    (persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | coupleID),

  autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)



prior1 <- c(
  brms::set_prior("normal(0, 5)", class = "b"),
  brms::set_prior("normal(0, 20)", class = "Intercept", lb=0, ub=5), # range of the outcome scale
  
  brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0),

  brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1)
  #brms::set_prior("cauchy(0, 10)", class = "sigma", lb = 0)
)


df_minimal <- df_double[, c("userID", all.vars(as.formula(formula)))]

is_reactance <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = bernoulli(),
  #control = list(adapt_delta = 0.95, max_treedepth = 15),
  iter = 5000,
  warmup = 2000,
  chains = 4,
  cores = 4,
  seed = 7777,
  file = file.path(working_directory, "models_cache", "is_reactance")

)
pp_check(is_reactance, type='hist')
## Using 10 posterior draws for ppc type 'hist' by default.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

pp_check(is_reactance)
## Using 10 posterior draws for ppc type 'dens_overlay' by default.

try(loo(is_reactance))
## 
## Computed from 12000 by 755 log-likelihood matrix.
## 
##          Estimate   SE
## elpd_loo   -507.4 22.1
## p_loo       467.6 21.1
## looic      1014.9 44.2
## ------
## MCSE of elpd_loo is NA.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.6, 1.2]).
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. ESS
## (-Inf, 0.7]   (good)       0    0.0%   <NA>    
##    (0.7, 1]   (bad)      135   17.9%   <NA>    
##    (1, Inf)   (very bad) 620   82.1%   <NA>    
## See help('pareto-k-diagnostic') for details.
plot(is_reactance, ask = FALSE)

summarize_brms(
  is_reactance, 
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = T) %>%
  print_df(rows_to_pack = rows_to_pack)
OR l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 0.01* 0.00 0.61 1.000 6050.32 7352.81
Within-Person Effects
Daily perceived persuasion target -> target 0.57 0.17 1.76 1.001 5213.94 6546.91
Daily perceived persuasion target -> agent 4.56 0.93 32.98 1.003 3965.22 6309.03
Daily perceived pressure target -> target 48.19* 4.21 852.81 1.001 5268.32 6233.23
Daily perceived pressure target -> agent 2.36 0.11 59.23 1.000 6983.99 7542.84
Daily perceived pushing target -> target 5.98* 1.49 35.94 1.001 4902.62 5963.54
Daily perceived pushing target -> agent 0.70 0.09 4.89 1.000 6466.07 7702.55
Day 10.52 0.05 2451.97 1.000 7136.10 8024.49
Daily weartime NA NA NA NA NA NA
Daily perceived support target -> target 0.31* 0.10 0.82 1.001 4978.03 6477.69
Daily perceived support target -> agent 0.53 0.18 1.39 1.001 5401.61 6172.94
Is a weekend 0.14 0.01 2.43 1.000 6459.38 7673.06
JITAI received 4.68 0.14 159.81 1.000 6316.13 7448.09
Days post skilled support intervention 0.55 0.01 28.28 1.000 6288.79 7923.51
Between-Person Effects
Mean perceived persuasion target -> target 66.11 0.46 17024.80 1.001 5611.04 7517.88
Mean Perceived persuasion target -> agent 8.48 0.05 1612.05 1.000 7696.64 8290.67
Mean Perceived pressure target -> target 19482.91* 18.26 22559684.51 1.000 8692.18 8590.93
Mean Perceived pressure target -> agent 140.30 0.09 275758.29 1.000 8159.80 7863.27
Mean Perceived pushing target -> target 7.91 0.01 10571.02 1.001 8600.19 8750.39
Mean Perceived pushing target -> agent 0.00 0.00 2.31 1.001 8400.86 8827.26
Mean weartime NA NA NA NA NA NA
Difference study group 2 5.46 0.05 833.67 1.001 5821.40 7469.84
Difference study group 3 0.01 0.00 1.21 1.001 6886.18 7868.72
Random Effects
sd(Intercept) 4.71 2.70 7.09 1.00 5000.79 6182.43
sd(Daily perceived persuasion target -> target) 1.00 0.04 2.58 1.00 2080.97 4750.12
sd(Daily perceived persuasion target -> agent) 2.29 0.69 4.23 1.00 3604.76 3629.11
sd(Daily perceived pressure target -> target) 2.04 0.09 5.04 1.00 2538.18 5173.40
sd(Daily perceived pressure target -> agent) 1.53 0.06 4.27 1.00 5985.31 6525.88
sd(Daily perceived pushing target -> target) 1.31 0.08 3.06 1.00 3341.40 4948.99
sd(Daily perceived pushing target -> agent) 0.99 0.04 2.89 1.00 5925.78 7178.45
Additional Parameters
ar[1] 0.10 -0.09 0.29 1.00 2142.56 3615.44
nu NA NA NA NA NA NA
shape NA NA NA NA NA NA
sderr 10.96 7.06 16.02 1.00 3114.28 5302.78
sigma NA NA NA NA NA NA

Report All Models

if (report_ordinal) {
  model_rows_random_final <- model_rows_random_ordinal
  model_rows_fixed_final <- model_rows_fixed_ordinal
  model_rownames_fixed_final <- model_rownames_fixed_ordinal
  model_rownames_random_final <- model_rownames_random_ordinal
  rows_to_pack_final <- rows_to_pack_ordinal
} else {
  model_rows_random_final <- model_rows_random
  model_rows_fixed_final <- model_rows_fixed
  model_rownames_fixed_final <- model_rownames_fixed
  model_rownames_random_final <- model_rownames_random
  rows_to_pack_final <- rows_to_pack
}



all_models <- report_side_by_side(
  pa_sub,
  pa_obj_log,
  mood,
  reactance,
  is_reactance,
  
  model_rows_random = model_rows_random_final,
  model_rows_fixed = model_rows_fixed_final,
  model_rownames_random = model_rownames_random_final,
  model_rownames_fixed = model_rownames_fixed_final
) 
## [1] "pa_sub"
## [1] "pa_obj_log"
## [1] "mood"
## [1] "reactance"
## [1] "is_reactance"
# pretty printing
summary_all_models <- all_models %>%
  print_df(rows_to_pack = rows_to_pack_final) %>%
  add_header_above(
    c(" ", "Subjective MVPA" = 2, 
      "Device-Based MVPA" = 2, 
      "Mood" = 2,
      "Reactance Gaussian" = 2, 
      "Reactance Dichotome" = 2)
  )

export_xlsx(summary_all_models, 
            rows_to_pack = rows_to_pack_final,
            file.path(working_directory, "Output", "AllCovariates_SensCovariates.xlsx"),
            merge_option = 'both', 
            simplify_2nd_row = TRUE,
            colwidths = c(40, 7.4, 12.85, 7.4, 12.85,7.4, 12.85,7.4, 12.85,7.4, 12.85),
            line_above_rows = c(1,2,3,28),
            line_below_rows = c(-1))

summary_all_models
Subjective MVPA
Device-Based MVPA
Mood
Reactance Gaussian
Reactance Dichotome
IRR pa_sub 95% CI pa_sub exp(Est.) pa_obj_log 95% CI pa_obj_log b mood 95% CI mood b reactance 95% CI reactance OR is_reactance 95% CI is_reactance
Intercept 26.72* [17.32, 41.49] 121.23* [100.82, 145.60] 4.66* [ 4.33, 4.98] 0.69* [ 0.45, 0.94] 0.01* [ 0.00, 0.61]
Within-Person Effects
Daily perceived persuasion target -> target 1.05 [ 0.94, 1.18] 1.01 [ 0.98, 1.04] -0.04* [-0.07, 0.00] -0.02 [-0.08, 0.04] 0.57 [ 0.17, 1.76]
Daily perceived persuasion target -> agent 1.04 [ 0.93, 1.17] 1.00 [ 0.97, 1.03] -0.01 [-0.05, 0.03] 0.02 [-0.05, 0.09] 4.56 [ 0.93, 32.98]
Daily perceived pressure target -> target 0.94 [ 0.71, 1.28] 0.95 [ 0.89, 1.01] -0.06 [-0.18, 0.05] 0.25* [ 0.04, 0.46] 48.19* [ 4.21, 852.81]
Daily perceived pressure target -> agent 1.21 [ 0.90, 1.67] 0.98 [ 0.92, 1.04] -0.03 [-0.17, 0.09] 0.13 [-0.06, 0.36] 2.36 [ 0.11, 59.23]
Daily perceived pushing target -> target 1.08 [ 0.89, 1.34] 1.02 [ 0.97, 1.06] 0.00 [-0.06, 0.06] 0.11* [ 0.02, 0.20] 5.98* [ 1.49, 35.94]
Daily perceived pushing target -> agent 1.06 [ 0.90, 1.27] 1.02 [ 0.98, 1.06] 0.05 [-0.01, 0.10] 0.00 [-0.09, 0.08] 0.70 [ 0.09, 4.89]
Day 0.96 [ 0.62, 1.46] 0.95 [ 0.84, 1.07] 0.24* [ 0.03, 0.46] 0.13 [-0.24, 0.50] 10.52 [ 0.05, 2451.97]
Daily weartime NA NA 1.00* [ 1.00, 1.00] NA NA NA NA NA NA
Daily perceived support target -> target 1.25* [ 1.17, 1.35] 1.04* [ 1.02, 1.05] 0.08* [ 0.05, 0.10] -0.08* [-0.13, -0.02] 0.31* [ 0.10, 0.82]
Daily perceived support target -> agent 1.21* [ 1.11, 1.31] 1.02* [ 1.00, 1.04] 0.03* [ 0.00, 0.05] -0.02 [-0.08, 0.03] 0.53 [ 0.18, 1.39]
Is a weekend 1.22* [ 1.01, 1.50] 1.05* [ 1.01, 1.10] 0.10* [ 0.04, 0.17] -0.12 [-0.28, 0.03] 0.14 [ 0.01, 2.43]
JITAI received 0.75* [ 0.58, 0.97] 0.93* [ 0.88, 0.98] -0.08* [-0.16, -0.01] -0.01 [-0.22, 0.20] 4.68 [ 0.14, 159.81]
Days post skilled support intervention 0.86 [ 0.62, 1.19] 1.03 [ 0.94, 1.12] 0.01 [-0.14, 0.17] -0.09 [-0.34, 0.17] 0.55 [ 0.01, 28.28]
Between-Person Effects
Mean perceived persuasion target -> target 1.65 [ 0.83, 3.26] 1.07 [ 0.80, 1.46] 0.38 [-0.16, 0.94] 0.05 [-0.29, 0.39] 66.11 [ 0.46, 17024.80]
Mean Perceived persuasion target -> agent 1.17 [ 0.60, 2.32] 0.94 [ 0.69, 1.30] 0.30 [-0.25, 0.85] 0.06 [-0.32, 0.44] 8.48 [ 0.05, 1612.05]
Mean Perceived pressure target -> target 0.54 [ 0.24, 1.21] 1.02 [ 0.73, 1.42] -0.32 [-0.89, 0.24] 0.58* [ 0.20, 0.97] 19482.91* [18.26, 22559684.51]
Mean Perceived pressure target -> agent 0.42* [ 0.19, 0.95] 0.98 [ 0.72, 1.34] -0.28 [-0.85, 0.29] 0.24 [-0.18, 0.66] 140.30 [ 0.09, 275758.29]
Mean Perceived pushing target -> target 1.53 [ 0.55, 4.34] 1.02 [ 0.66, 1.57] 0.17 [-0.60, 0.94] -0.19 [-0.69, 0.32] 7.91 [ 0.01, 10571.02]
Mean Perceived pushing target -> agent 2.03 [ 0.73, 5.72] 1.33 [ 0.85, 2.05] 0.28 [-0.50, 1.05] -0.56 [-1.10, 0.00] 0.00 [ 0.00, 2.31]
Mean weartime NA NA 1.00 [ 1.00, 1.00] NA NA NA NA NA NA
Difference study group 2 0.80 [ 0.46, 1.37] 0.89 [ 0.69, 1.15] -0.23 [-0.67, 0.22] 0.08 [-0.19, 0.34] 5.46 [ 0.05, 833.67]
Difference study group 3 1.12 [ 0.64, 1.94] 0.97 [ 0.75, 1.26] 0.36 [-0.11, 0.81] -0.33* [-0.61, -0.05] 0.01 [ 0.00, 1.21]
Random Effects
sd(Intercept) 0.64 [ 0.47, 0.87] 0.31 [0.23, 0.40] 0.55 [0.42, 0.72] 0.19 [ 0.03, 0.35] 4.71 [ 2.70, 7.09]
sd(Daily perceived persuasion target -> target) 0.07 [ 0.00, 0.21] 0.05 [0.02, 0.08] 0.03 [0.00, 0.07] 0.04 [ 0.00, 0.11] 1.00 [ 0.04, 2.58]
sd(Daily perceived persuasion target -> agent) 0.07 [ 0.00, 0.18] 0.05 [0.02, 0.08] 0.06 [0.01, 0.11] 0.04 [ 0.00, 0.13] 2.29 [ 0.69, 4.23]
sd(Daily perceived pressure target -> target) 0.17 [ 0.01, 0.53] 0.05 [0.00, 0.14] 0.12 [0.01, 0.30] 0.39 [ 0.22, 0.61] 2.04 [ 0.09, 5.04]
sd(Daily perceived pressure target -> agent) 0.18 [ 0.01, 0.51] 0.03 [0.00, 0.10] 0.17 [0.02, 0.36] 0.21 [ 0.01, 0.57] 1.53 [ 0.06, 4.27]
sd(Daily perceived pushing target -> target) 0.26 [ 0.01, 0.56] 0.06 [0.00, 0.14] 0.08 [0.01, 0.17] 0.12 [ 0.01, 0.23] 1.31 [ 0.08, 3.06]
sd(Daily perceived pushing target -> agent) 0.11 [ 0.00, 0.30] 0.03 [0.00, 0.08] 0.04 [0.00, 0.12] 0.05 [ 0.00, 0.15] 0.99 [ 0.04, 2.89]
Additional Parameters
ar[1] 0.03 [-0.94, 0.94] 0.30 [0.26, 0.33] 0.45 [0.42, 0.48] 0.02 [-0.06, 0.11] 0.10 [-0.09, 0.29]
nu NA NA NA NA NA NA NA NA NA NA
shape 0.14 [ 0.13, 0.15] NA NA NA NA NA NA NA NA
sderr 0.05 [ 0.00, 0.14] NA NA NA NA NA NA 10.96 [ 7.06, 16.02]
sigma NA NA 0.55 [0.54, 0.57] 0.86 [0.84, 0.88] 0.93 [ 0.88, 0.98] NA NA 
report::report_system()

Analyses were conducted using the R Statistical language (version 4.3.2; R Core Team, 2023) on Windows 11 x64 (build 22635)

report::cite_packages()